M J Wolf1, S Easteal, M Kahn, B D McKay, L S Jermiin. 1. Department of Mathematics and Computer Science, Bemidji State University, Bemidji, MN 56601-2699, USA. mjwolf@whitetail.bemidji.msus.edu
Abstract
MOTIVATION: Maximum-likelihood analysis of nucleotide and amino acid sequences is a powerful approach for inferring phylogenetic relationships and for comparing evolutionary hypotheses. Because it is a computationally demanding and time-consuming process, most algorithms explore only a minute portion of tree-space, with the emphasis on finding the most likely tree while ignoring the less likely, but not significantly worse, trees. However, when such trees exist, it is equally important to identify them to give due consideration to the phylogenetic uncertainty. Consequently, it is necessary to change the focus of these algorithms such that near optimal trees are also identified. RESULTS: This paper presents the Advanced Stepwise Addition Algorithm for exploring tree-space and two algorithms for generating all binary trees on a set of sequences. The Advanced Stepwise Addition Algorithm has been implemented in TrExML, a phylogenetic program for maximum-likelihood analysis of nucleotide sequences. TrExML is shown to be more effective at finding near optimal trees than a similar program, fastDNAml, implying that TrExML offers a better approach to account for phylogenetic uncertainty than has previously been possible. A program, TreeGen, is also described; it generates binary trees on a set of sequences allowing for extensive exploration of tree-space using other programs. AVAILABILITY: TreeGen, TrExML, and the sequence data used to test the programs are available from the following two WWW sites: http://whitetail.bemidji.msus. edu/trexml/and http://jcsmr.anu.edu.au/dmm/humgen.+ ++html.
MOTIVATION: Maximum-likelihood analysis of nucleotide and amino acid sequences is a powerful approach for inferring phylogenetic relationships and for comparing evolutionary hypotheses. Because it is a computationally demanding and time-consuming process, most algorithms explore only a minute portion of tree-space, with the emphasis on finding the most likely tree while ignoring the less likely, but not significantly worse, trees. However, when such trees exist, it is equally important to identify them to give due consideration to the phylogenetic uncertainty. Consequently, it is necessary to change the focus of these algorithms such that near optimal trees are also identified. RESULTS: This paper presents the Advanced Stepwise Addition Algorithm for exploring tree-space and two algorithms for generating all binary trees on a set of sequences. The Advanced Stepwise Addition Algorithm has been implemented in TrExML, a phylogenetic program for maximum-likelihood analysis of nucleotide sequences. TrExML is shown to be more effective at finding near optimal trees than a similar program, fastDNAml, implying that TrExML offers a better approach to account for phylogenetic uncertainty than has previously been possible. A program, TreeGen, is also described; it generates binary trees on a set of sequences allowing for extensive exploration of tree-space using other programs. AVAILABILITY: TreeGen, TrExML, and the sequence data used to test the programs are available from the following two WWW sites: http://whitetail.bemidji.msus. edu/trexml/and http://jcsmr.anu.edu.au/dmm/humgen.+ ++html.
Authors: R S Millen; R G Olmstead; K L Adams; J D Palmer; N T Lao; L Heggie; T A Kavanagh; J M Hibberd; J C Gray; C W Morden; P J Calie; L S Jermiin; K H Wolfe Journal: Plant Cell Date: 2001-03 Impact factor: 11.277
Authors: G J Adcock; E S Dennis; S Easteal; G A Huttley; L S Jermiin; W J Peacock; A Thorne Journal: Proc Natl Acad Sci U S A Date: 2001-01-16 Impact factor: 11.205
Authors: Andrew Butterfield; Vivek Vedagiri; Edward Lang; Cath Lawrence; Matthew J Wakefield; Alexander Isaev; Gavin A Huttley Journal: BMC Bioinformatics Date: 2004-01-05 Impact factor: 3.169
Authors: Rob Knight; Peter Maxwell; Amanda Birmingham; Jason Carnes; J Gregory Caporaso; Brett C Easton; Michael Eaton; Micah Hamady; Helen Lindsay; Zongzhi Liu; Catherine Lozupone; Daniel McDonald; Michael Robeson; Raymond Sammut; Sandra Smit; Matthew J Wakefield; Jeremy Widmann; Shandy Wikman; Stephanie Wilson; Hua Ying; Gavin A Huttley Journal: Genome Biol Date: 2007 Impact factor: 13.583